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SAM - Statistics and Machine Learning Team.

Team leader : Anatoli Juditsky

Administrative assistant : Juana Dos Santos

Overview

The SAM team does research in a number of different areas related to adaptive data analysis and processing, notably: mathematical statistics; machine learning; signal and image processing, computational finance.

The methods developed are applied in a wide range of areas including: bioinformatics, biostatistics and pharmacometry; health sciences; medical signal and image processing; image interpretation; environmental sciences; the analysis and optimization of electronic circuits, finance ...

Research themes

Mathematical statistics: dimensionality reduction; variable selection; non-parametric estimation; change point detection; model selection; generalized linear models; experimental design for mixed effect models; the analysis of functional data; curve classification; dynamical systems and long time behavior.

Machine learning: algorithms for large-scale learning and optimization; unsupervised and weakly supervised learning; structured output methods and complex models.

Computational finance: parallel computing, adaptative Monte-Carlo methods.

Signal and image processing: discriminant feature sets; pattern recognition and motion analysis for content understanding; sparse and adaptive signal processing; wavelet based methods.